As Philadelphia strives to meet greenhouse gas emissions goals established in its 2050 Plan, a better understanding
of how zoning can play a role in managing building energy use could set the city up for success. Researchers in
Drexel University’s College of Engineering are hoping a machine learning model they’ve developed can
support these efforts by helping to predict how energy consumption will change as neighborhoods evolve.
In 2017, the city set
a goal of becoming carbon neutral by 2050, led in large part by a reduction in greenhouse gas emissions from
building
energy use – which accounted for nearly three-quarters of Philadelphia’s carbon footprint at the
time. But the key to meeting this mark lies not just in establishing sustainable energy use practices for
current buildings, but also incorporating energy use projections into zoning decisions that will direct future
development.
And the challenge for Philadelphia,
one of the oldest cities in the country, is that building types vary widely — as does their energy use. So
planning for more efficient energy use at the City level is not a problem with a one-size-fits-all
solution.
“For Philadelphia in
particular, neighborhoods vary so much from place to place in prevalence of certain housing features and zoning
types that it’s important to customize energy programs for each neighborhood, rather than trying to enact
blanket policies for carbon reduction across the entire city or county,” said Simi Hoque, PhD, a professor in the
College of Engineering who led research into using machine learning
for granular energy-use modeling
recently published in the journal Energy
& Buildings.
Hoque’s team believes existing
machine learning programs, properly deployed, can provide some clarity on how zoning decisions could affect
future greenhouse gas emissions from buildings.
“Right now there is a huge
volume of energy use data, but it’s often just too inconsistent and messy to be reasonably put to use. For
example, one dataset corresponding to certain housing characteristics may have usable energy
estimates, but another dataset corresponding to socioeconomic features is missing too many values to be
usable,” she said. “Machine learning is well equipped to handle this challenge because they can
iteratively learn and improve through the training process to reduce bias and variance despite these data
limitations.”
To glean information from the
disjointed data, the team developed a process using two machine learning programs — one that can tease out
patterns from massive tranches of data and use them to make projections about future energy and a second that
can pinpoint the details in the model that likely had the greatest effect on changing the projections.
First they trained a deep-learning
program, called Extreme Gradient Boosting (XGBoost), with volumes of commercial and residential energy-use data
for Philadelphia from the U.S. Energy Information’s Residential Energy Consumption Survey and Commercial
Buildings Energy Consumption Survey for 2015, as well as the city’s demographic and socioeconomic data
from the U.S. Census Bureau’s American Communities Survey for that time period.
The program learned enough from the
data that it could draw correlations between a laundry list of variables, such as density of buildings,
population of a given area, building square footage, number of occupants, how many days heating or air
conditioning was used, and energy use for each house or building.
While deep learning models like
XGBoost are very useful for making informed forecasts, given a large and inconsistent set of data, their methods
can be obscured by the complexity of the operations they perform. But to be a useful tool for guiding planners,
the team needed to unpack the so-called “black box” program enough to turn its projections into
recommendations.
To do it, they employed a Shapley
additive explanations analysis, an assessment used in game theory to distribute credit among factors that
contributed to an outcome. This allowed them to suss out how much a change in building density or square
footage, for example, factored into the program’s projection.
“Machine learning
models like XGBoost learn how to chug through datasets to fulfill a specific task — like generating a
reliable forecast of a system — but they do not claim to really understand or represent the on-the-ground
relationships that underlie a phenomenon,” Hoque said. “And while a Shapley analysis cannot tell us
which features have the greatest impact on energy use, it can explain which features had the greatest impact on
the model’s energy use prediction, which is still quite a useful piece of
information.”
Then the team put the model to the
test by providing input data from a hypothetical scenario proposed by the Delaware Valley Regional Planning
Commission that estimated continuing economic development in Philadelphia through the year 2045. The scenario
suggested a 17% population increase with a commensurate increase in households, and it presents a number of
different possibilities for employment and income by region throughout the city.
For each scenario, the model
projected how new residential and commercial development would change greenhouse gas emissions from building
energy use throughout 11 different parts of the city and which variables played prominent roles in making the
projections.
Looking specifically at residential
energy use for the 2045 scenario, the program suggested that six of the 11 areas would decrease their energy use
– mostly lower-income regions. While mixed-income regions, like the northernmost part of the city,
including Oak Lane, would likely see an increase in energy use.
According to the Shapley analysis,
the presence of single-family attached (lower energy use) versus detached (higher energy use) dwellings played
an important role in the projections, with high monthly electricity cost, lot sizes of less than one acre, and
lower number of rooms per building all contributing to lower energy use projections.
“Overall, the residential energy prediction model finds that
features related to lower building intensity relate to lower energy consumption estimates in the model, for
example lower lot acreage, lower number of rooms per unit,” they wrote. “These results give reason
to reinvestigate the effects of upzoning policies, commonly present as an affordable housing solution in
Philadelphia and other cities across the U.S., and subsequent changes in energy use for these
areas.”
On the commercial side of the
scenario, the machine learning model did not project much change in energy use under the 2045 conditions —
energy use for the largest commercial buildings remained high. And while it was limited to looking at just six
variables — square footage, number of employees, number of floors, heating degree days, cooling degree
days, and the principal activity of the building — due to the available data in the training set, the
Shapley analysis pointed to building square footage and number of employees as the most important predictors of
energy use for most types of commercial buildings.
“With respect to the commercial sector, the study suggests that
commercial buildings in the top quantiles of square footage and employee count should be the primary targets for
energy reduction programs,” the authors wrote. “The research posits an approximate threshold of
10,000 square feet of total building area, with buildings over that marker being prioritized due to their
disproportionate influence on the energy prediction of the model.”
While the researchers caution against
assuming a direct link between variables and energy use changes in the model, they suggest that it is still
quite useful because of its ability to give planners both a high-level and granular look at the interplay of
zoning decisions and development and their effect on energy use.
“I see a lot of potential in using machine learning models like
XGBoost to forecast energy use increases or decreases due to new construction projects or policy changes,”
Hoque said. “For example, building a new rail line in a neighborhood may change the demographics and
employment of a neighborhood, and our methods would be ideal for incorporating that information in the context
of an energy prediction model.”
The team acknowledges that more
testing is necessary and the program will only improve as it is provided with additional data. They suggest that
a next step for the research would be to focus on areas of the city with known high energy use and perform a
Shapely analysis to discern some of the factors that could be contributing to it.
“We hope this will provide a
resource for future researchers and policy makers so they don’t have to scope through the
entire city of Philadelphia, but can hone in on neighborhoods and variables which we have flagged as areas of
potential importance,” Hoque said. “Ideally, future studies would use more interpretable methods to
test whether these features really correspond to higher or lower energy estimates in a given area.”
In addition to Hoque, Shideh Shams Amiri and Maya Mueller,
doctoral students in Drexel’s College of Engineering, participated in this research.
Read the full paper here: https://www.sciencedirect.com/science/article/pii/S0378778823001950